scholarly journals Offline Signature Verification System Using SVM Classifier with Image Pre-processing Steps and SURF Algorithm

2021 ◽  
Vol 2107 (1) ◽  
pp. 012069
Author(s):  
Li Wen Goon ◽  
Swee Kheng Eng
Keyword(s):  
Feature Extraction ◽  
Noise Removal ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Speeded Up Robust Features ◽  
Verification System ◽  
Offline Signature Verification ◽  
Average Accuracy ◽  
Processing Steps

Abstract A signature is a mark or name that represents the identity of the people and the Signature Verification System (SVS) is used to validate the identity of people. The signature verification system is mostly used for bank cheques, vouchers, intelligence agencies and others. There are two types of SVS which are online and offline signature verification systems. The paper deals with an offline signature verification system. The proposed system consists of four main stages, (i) image acquisition, (ii) image pre-processing, (iii) feature extraction and (iv) classification. The image pre-processing steps involved binarization, noise removal using Gaussian filter and image resizing and thinning. In the feature extraction stage, Bag-of-Features with the Speeded Up Robust Features (SURF) extractor was utilized. In the third stage, the Support Vector Machine (SVM) classifier is used. Lastly, the confusion matrix and the verification rate were used to evaluate the performance of the classifier. In this paper, we implement and compare the performance of the signature verification system without entering the user ID and the signature verification system entering the user ID. For the ratio of 75% and 25% of the training and testing, respectively, the average accuracy for the signature verification system without entering the user ID is 71.36%, whereas the average accuracy for the signature verification system entering the user ID is 79.55%.

FREEMAN CHAIN CODE AS REPRESENTATION IN OFFLINE SIGNATURE VERIFICATION SYSTEM

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Aini Najwa Azmi ◽  
Dewi Nasien ◽  
Azurah Abu Samah
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Rejection Rate ◽  
Data Representation ◽  
Signature Verification ◽  
Global Features ◽  
Chain Code ◽  
Verification System ◽  
Offline Signature Verification ◽  
Handwritten Signature

Over recent years, there has been an explosive growth of interest in the pattern recognition. For example, handwritten signature is one of human biometric that can be used in many areas in terms of access control and security. However, handwritten signature is not a uniform characteristic such as fingerprint, iris or vein. It may change to several factors; mood, environment and age. Signature Verification System (SVS) is a part of pattern recognition that can be a solution for such situation. The system can be decomposed into three stages: data acquisition and preprocessing, feature extraction and verification. This paper presents techniques for SVS that uses Freeman chain code (FCC) as data representation. In the first part of feature extraction stage, the FCC was extracted by using boundary-based style on the largest contiguous part of the signature images. The extracted FCC was divided into four, eight or sixteen equal parts. In the second part of feature extraction, six global features were calculated. Finally, verification utilized k-Nearest Neighbour (k-NN) to test the performance. MCYT bimodal database was used in every stage in the system. Based on our systems, the best result achieved was False Rejection Rate (FRR) 14.67%, False Acceptance Rate (FAR) 15.83% and Equal Error Rate (EER) 0.43% with shortest computation, 7.53 seconds and 47 numbers of features.

scholarly journals Writer Independent Manipuri Offline Signature Verification using Transfer Learning Technique of Convolutional Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 76-80
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Ability ◽  
Signature Verification ◽  
Support Vector ◽  
Svm Classifier ◽  
Powerful Learning ◽  
Offline Signature Verification ◽  
Learning Technique ◽  
Handwritten Signature

Automatic Signature Verification system is used to verify whether a signature is genuine or forged. Forged Signatures are those signatures that a person produced by imitating the signature of another person. Automatic Signature Verification is very important as a person’s handwritten signature is used everywhere to authenticate themselves and there is not very much difference between a genuine signature and the imitation of it, i.e. a forged signature. In this work, signature verification is done using different pre-trained Convolutional Neural Networks (CNNs). Convolutional Neural Network has powerful learning ability, and it can be used to distinguish between a genuine and a forged signature automatically. In this experiment, Manipuri signature dataset was used, the dataset was prepared originally and it contains 729 genuine signatures and 243 forged signatures. Features were extracted from pre-trained networks and classification was done using binary Support Vector Machine (SVM) classifier and the performances of the networks were compared. And according to the experiment we achieved a classification accuracy of 84.7 using VGG19 features, accuracy of 86.8 using VGG16 features and accuracy of 81.9 using Alexnet features.

scholarly journals An Improved Geo-Textural Based Feature Extraction Vector For Offline Signature Verification

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Kennedy Gyimah ◽  
Justice Kwame Appati ◽  
Kwaku Darkwah ◽  
Kwabena Ansah
Keyword(s):  
Feature Extraction ◽  
Feature Vector ◽  
Least Square Method ◽  
Kernel Functions ◽  
Least Square ◽  
Signature Verification ◽  
Support Vector ◽  
Offline Signature Verification ◽  
Occurrence Matrix ◽  
Handwritten Signature

In the field of pattern recognition, automatic handwritten signature verification is of the essence. The uniqueness of each person’s signature makes it a preferred choice of human biometrics. However, the unavoidable side-effect is that they can be misused to feign data authenticity. In this paper, we present an improved feature extraction vector for offline signature verification system by combining features of grey level occurrence matrix (GLCM) and properties of image regions. In evaluating the performance of the proposed scheme, the resultant feature vector is tested on a support vector machine (SVM) with varying kernel functions. However, to keep the parameters of the kernel functions optimized, the sequential minimal optimization (SMO) and the least square method was used. Results of the study explained that the radial basis function (RBF) coupled with SMO best support the improved featured vector proposed.

Online Direction Classification and Recognition Using an Intuitive Tactile Communication

2014 ◽  
Vol 543-547 ◽  
pp. 1542-1545
Author(s):  
Hao Ying Wu ◽  
Kai Fan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Average Accuracy ◽  
Vector Machines ◽  
Tactile Communication ◽  
Low Costs ◽  
Robot Cooperation

This paper proposed an online direction classifying method for constructing an intuitive tactile communication during human-robot cooperation. The proposed approach abstracts a suitable feature set from a tactile array sensor equipped on a hand-bar. This lower computation feature extraction method analyze the weighting values concerned with oriental information from principle component analysis (PCA), together with support vector machines (SVM) classifier for direction classification and recognition. Experimental results showed an average accuracy of 96.3% and a low costs of 512μs with respect to different handle gestures of the 6 touch directions, which is practicable utilized for human-robot cooperation based on tactile recognition.

scholarly journals Convolutional Neural Network for Offline Signature Verification via Multiple Classifiers

2021 ◽  
Author(s):  
Fadi Mohammad Alsuhimat ◽  
Fatma Susilawati Mohamad
Keyword(s):  
Signature Verification ◽  
Classification Error ◽  
Support Vector ◽  
Svm Classifier ◽  
Digital Environment ◽  
K Nearest Neighbors ◽  
Offline Signature Verification ◽  
Error Classification ◽  
Run Time ◽  
Computerized System

The signature process is one of the most significant processes used by organizations to preserve the security of information and protect it from unwanted penetration or access. As organizations and individuals move into the digital environment, there is an essential need for a computerized system able to distinguish between genuine and forged signatures in order to protect people's authorization and decide what permissions they have. In this paper, we used Pre-Trained CNN for extracts features from genuine and forged signatures, and three widely used classification algorithms, SVM (Support Vector Machine), NB (Naive Bayes) and KNN (k-nearest neighbors), these algorithms are compared to calculate the run time, classification error, classification loss and accuracy for test-set consist of signature images (genuine and forgery). Three classifiers have been applied using (UTSig) dataset; where run time, classification error, classification loss and accuracy were calculated for each classifier in the verification phase, the results showed that the SVM and KNN got the best accuracy (76.21), while the SVM got the best run time (0.13) result among other classifiers, therefore the SVM classifier got the best result among the other classifiers in terms of our measures.

scholarly journals BLOCKWISE BINARY PATTERN: A ROBUST AND AN EFFICIENT APPROACH FOR OFFLINE SIGNATURE VERIFICATION

2017 ◽  
Vol XLII-2/W4 ◽  
pp. 227-232 ◽  
Author(s):  
B. H. Shekar ◽  
B. Pilar ◽  
K. D. S. Sunil
Keyword(s):  
Feature Extraction ◽  
Local Binary Pattern ◽  
Binary Sequence ◽  
Signature Verification ◽  
Svm Classifier ◽  
Efficient Approach ◽  
Regional Language ◽  
Clockwise Direction ◽  
Offline Signature Verification ◽  
Central Pixel

This paper presents a variant of local binary pattern called Blockwise Binary Pattern (BBP) for the offline signature verification. The proposed approach has three major phases : Preprocessing, Feature extraction and Classification. In the feature extraction phase, the signature is divided into 3 x 3 neighborhood blocks. A BBP value for central pixel of each block is computed by considering its 8 neighboring pixels and the 3 x 3 block is replaced by this central pixel. To compute BBP value for each block, a binary sequence is formed by considering 8 neighbors of the central pixel, by following the pixels in a anti-clockwise direction. Then the minimum decimal equivalent of this binary sequence is computed and this value is assigned to the central pixel. The central pixel is merged with the neighboring 8 pixels representing the 3 X 3 neighborhood block. This method is found to be invariant to rotation, scaling and shift of the signature. The features are stored in the form of normalized histogram. The SVM classifier is used for the signature verification. Experiments have been performed on standard signature datasets namely CEDAR and GPDS which are publicly available English signature datasets and on MUKOS, a regional language (Kannada) dataset and compared with the well-known approaches to exhibit the performance of the proposed approach.

A Structural Analysis Based Feature Extraction Method for OCR System For Myanmar Printed Document Images

2012 ◽  
Vol 2 (1) ◽  
pp. 16-41 ◽  
Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun
Keyword(s):  
Feature Extraction ◽  
Structural Analysis ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Extraction Method ◽  
Recognition Performance ◽  
Extraction Methods ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.

scholarly journals Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

2018 ◽  
Vol 10 (7) ◽  
pp. 1123 ◽  
Author(s):  
Yuhang Zhang ◽  
Hao Sun ◽  
Jiawei Zuo ◽  
Hongqi Wang ◽  
Guangluan Xu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Loss Function ◽  
Extraction Method ◽  
Generative Adversarial Networks ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Extraction Method ◽  
Adversarial Networks ◽  
Aircraft Type

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.

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